Systems and methods for enhancing workflow efficiency in a healthcare management system

At least one non-transitory computer-readable storage medium with instructions stored thereon that, in response to execution by at least one processor, cause the at least one processor to:

• at least one memory with instructions stored thereon;
• and
• at least one processor in communication with the at least one memory, wherein the instructions, when executed by the at least one processor, cause the at least one processor to:
• receive a plurality of records;
• determine a first subset of the plurality of records that include first machine readable objects and a second subset of the plurality of records that include machine unreadable objects;
• convert the second subset into an updated second subset including second machine readable objects;
• embed information in the first subset and the updated second subset by meta tagging the information into the first subset and the second subset to characterize the first subset and the updated second subset;
• extract a plurality of data elements from the first subset and the updated second subset based on the embedded information and a set of similarity rules for determining which data to cluster into a plurality of clusters;
• transmit the plurality of data elements to a concept model;
• receive a plurality of work items from the concept model;
• and
• route the work items through at least one workflow, wherein work items associated with at least one of higher probability of acceptance or higher reimbursement amounts are routed before work items with at least one of lower probability of acceptance or lower reimbursement amounts in order to achieve a workflow objective of maximizing reimbursement of medical claims.

A method for health information management implemented by at least one processor in communication with at least one memory, the method comprising:

• receiving a plurality of records;
• determining a first subset of the plurality of records that include first machine readable objects and a second subset of the plurality of records that include machine unreadable objects;
• converting the second subset into an updated second subset including second machine readable objects;
• embedding information in the first subset and the updated second subset by meta tagging the information into the first subset and the second subset to characterize the first subset and the updated second subset;
• extracting a plurality of data elements from the first subset and the updated second subset based on the embedded information and a set of similarity rules for determining which data to cluster into a plurality of clusters;
• transmitting the plurality of data elements to a concept model;
• receiving a plurality of work items from the concept model;
• and
• routing the work items through at least one workflow, wherein work items associated with at least one of higher probability of acceptance or higher reimbursement amounts are routed before work items with at least one of lower probability of acceptance or lower reimbursement amounts in order to achieve a workflow objective of maximizing reimbursement of medical claims.